scholarly journals Demand Forecast on the Orchard Cold Chain Logistics in Hunan Province Based on Cognitive Neuroscience

2022 ◽  
Vol 16 ◽  
pp. 257-263
Author(s):  
Ying Fu ◽  
Xiangpeng Zeng ◽  
Yihua Li ◽  
Yiming Wen ◽  
Xiaowei Wen
Keyword(s):  
Cognitive Neuroscience ◽  
Government Policy ◽  
Bp Neural Network ◽  
Hunan Province ◽  
Cold Chain ◽  
Demand Forecast ◽  
Logistics System ◽  
New Perspective ◽  
Logistics Demand ◽  
Bp Neural Network Model

How to scientifically and effectively predict the cold chain logistics demand and provide basis for decision making has always been the focus of forestry and orchard logistics research. From the learning environment of neurons, cognitive neuroscience provides a new perspective for forecasting the demand for cold chain logistics. This paper uses the cognitive neuroscience theory to construct a BP neural network model containing two hidden layers to predict the cold chain logistics demand of the forestry and orchard industry in Hunan province in 2017-2021. Suggestions are then given from the aspects of cold chain logistics construction, transportation infrastructure construction, government policy, enterprise and industry according to the prediction results, thus, providing a theoretical basis for the planning of the cold chain logistics system of Hunan province in a certain period of time, as well as references for the development of cold chain logistics in other parts of the country.

scholarly journals Demand Forecast on the Orchard Cold Chain Logistics in Hunan Province Based on Cognitive Neuroscience

2022 ◽  
Vol 16 ◽  
pp. 571-577
Author(s):  
Ying Fu ◽  
Xiangpeng Zeng ◽  
Yihua Li ◽  
Yiming Wen ◽  
Xiaowei Wen
Keyword(s):  
Cognitive Neuroscience ◽  
Government Policy ◽  
Bp Neural Network ◽  
Hunan Province ◽  
Cold Chain ◽  
Demand Forecast ◽  
Logistics System ◽  
New Perspective ◽  
Logistics Demand ◽  
Bp Neural Network Model

How to scientifically and effectively predict the cold chain logistics demand and provide basis for decision making has always been the focus of forestry and orchard logistics research. From the learning environment of neurons, cognitive neuroscience provides a new perspective for forecasting the demand for cold chain logistics. This paper uses the cognitive neuroscience theory to construct a BP neural network model containing two hidden layers to predict the cold chain logistics demand of the forestry and orchard industry in Hunan province in 2017-2021. Suggestions are then given from the aspects of cold chain logistics construction, transportation infrastructure construction, government policy, enterprise and industry according to the prediction results, thus, providing a theoretical basis for the planning of the cold chain logistics system of Hunan province in a certain period of time, as well as references for the development of cold chain logistics in other parts of the country.

scholarly journals Urban cold-chain logistics demand predicting model based on improved neural network model

2020 ◽  
Vol 11 ◽  
pp. 5
Author(s):  
Ying Chen ◽  
Qiuming Wu ◽  
Lijin Shao
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Fruits And Vegetables ◽  
Prediction Interval ◽  
Added Value ◽  
Cold Chain ◽  
Logistics Demand ◽  
Bp Neural Network Model

With the popularity of the Internet and mobile terminals, the development of e-commerce has become hotter. Therefore, e-commerce research starts to focus on the statistics and prediction of the cargo volume of logistics. This study briefly introduced the back-propagation (BP) neural network model and principal component analysis (PCA) method and combined them to obtain an improved PCA-BP neural network model. Then the traditional BP neural network model and the improved PCA-BP neural network model were used to perform the empirical analysis of the cold chain logistics demand of fruits and vegetables in city A from 2010 to 2018. The results showed that the main factors that affected the local cold chain logistics demand were the growth rate of GDP, the added value of primary industry, the planting area of fruits and vegetables, and the consumption price index of fruits and vegetables; both kinds of neural networks model could effectively predict the cold chain logistics demand, but the predicted value of the PCA-BP neural network model was more fitted with the actual value. The prediction error of the BP neural network model was larger, and the fluctuation was obvious within the prediction interval. Moreover, the time required for the prediction by the PCA-BP neural network model was less than that by the BP neural network model. In summary, the improved PCA-BP neural network model is faster and more accurate than the traditional BP model in predicting the cold chain logistics demand.

scholarly journals Regional logistics demand forecasting: a BP neural network approach

2021 ◽  
Author(s):  
Lijuan Huang ◽  
Guojie Xie ◽  
Wende Zhao ◽  
Yan Gu ◽  
Yi Huang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Demand Forecasting ◽  
Indicator System ◽  
Regional Logistics ◽  
Logistics Enterprises ◽  
Logistics Demand ◽  
Bp Neural Network Model

AbstractWith the rapid development of e-commerce, the backlog of distribution orders, insufficient logistics capacity and other issues are becoming more and more serious. It is very significant for e-commerce platforms and logistics enterprises to clarify the demand of logistics. To meet this need, a forecasting indicator system of Guangdong logistics demand was constructed from the perspective of e-commerce. The GM (1, 1) model and Back Propagation (BP) neural network model were used to simulate and forecast the logistics demand of Guangdong province from 2000 to 2019. The results show that the Guangdong logistics demand forecasting indicator system has good applicability. Compared with the GM (1, 1) model, the BP neural network model has smaller prediction error and more stable prediction results. Based on the results of the study, it is the recommendation of the authors that e-commerce platforms and logistics enterprises should pay attention to the prediction of regional logistics demand, choose scientific forecasting methods, and encourage the implementation of new distribution modes.

Optimization Research of the Comprehensive Coke Rate of Blast Furnace Based on the Operational Characteristic of Auxiliary Materials

2012 ◽  
Vol 260-261 ◽  
pp. 3-9 ◽  
Author(s):  
Hua Zhang ◽  
Zhao Hui Feng ◽  
Yan Hong Wang
Keyword(s):  
Neural Network ◽  
Blast Furnace ◽  
Influencing Factors ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Operational Characteristic ◽  
Coke Rate ◽  
New Perspective ◽  
Bp Neural Network Model

With the in-depth study on the blast furnace iron-making process and the operational characteristic of auxiliary materials in iron-making process, the comprehensive coke rate’s main influencing factors based on the operation characteristics of auxiliary materials were found. Then, a BP neural network model was used to simulate the mathematic mapping relationship between comprehensive coke rate and main influencing factors. Based on the established BP neural network model, through setting the comprehensive coke rate lowest as the goal and using the actual production data of a iron &steel company’s 6# blast furnace ,a genetic algorithm method is adopted to find the best optimal combination among the main influencing factors. The results show that after optimization calculation the comprehensive coke rate could be reduced about 35.85kg. A new perspective and a scientific method are proposed to realize the target of energy conservation and emission reduction in ironmaking process in this paper.

scholarly journals Traffic demand Forecast of online car-hailing based on BP Neural Network

2020 ◽  
Vol 214 ◽  
pp. 02035
Author(s):  
Junji Jiang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Transport Capacity ◽  
Demand Forecast ◽  
Traffic Demand ◽  
Travel Experience ◽  
Bp Neural Network Model

To accurately predict passengers’ demand for ride-hailing, increase transport capacity in some areas directionally, make it easier for passengers to book ride-hailing, and thus enhance passengers’ travel experience, based on BP neural network model, combined with 2015-Mak-2019 ride-hailing demand change data, and based on MATLAB platform, the demand trend of 20202024 is forecasted. The results show that the demand for shared cars and rides will increase rapidly. Demand for shared bikes and taxis is rising slowly.

Estimating nitrogen contents of apple leaves based on hyperspectral parameter models at different phenophases

2016 ◽  
Vol 6 (2) ◽  
pp. 942-952
Author(s):  
Xicun ZHU ◽  
Zhuoyuan WANG ◽  
Lulu GAO ◽  
Gengxing ZHAO ◽  
Ling WANG
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Shoot Growth ◽  
Hyperspectral Data ◽  
Estimation Model ◽  
Apple Leaves ◽  
Bp Neural Network Model ◽  
Nitrogen Contents

The objective of the paper is to explore the best phenophase for estimating the nitrogen contents of apple leaves, to establish the best estimation model of the hyperspectral data at different phenophases. It is to improve the apple trees precise fertilization and production management. The experiments were done in 20 orchards in the field, measured hyperspectral data and nitrogen contents of apple leaves at three phenophases in two years, which were shoot growth phenophase, spring shoots pause growth phenophase, autumn shoots pause growth phenophase. The study analyzed the nitrogen contents of apple leaves with its original spectral and first derivative, screened sensitive wavelengths of each phenophase. The hyperspectral parameters were built with the sensitive wavelengths. Multiple stepwise regressions, partial least squares and BP neural network model were adopted in the study. The results showed that 551 nm, 716 nm, 530 nm, 703 nm; 543 nm, 705 nm, 699 nm, 756 nm and 545 nm, 702 nm, 695 nm, 746 nm were sensitive wavelengths of three phenophases. R551+R716, R551*R716, FDR530+FDR703, FDR530*FDR703; R543+R705, R543*R705, FDR699+FDR756, FDR699*FDR756and R545+R702, R545*R702, FDR695+FDR746, FDR695*FDR746 were the best hyperspectral parameters of each phenophase. Of all the estimation models, the estimated effect of shoot growth phenophase was better than other two phenophases, so shoot growth phenophase was the best phenophase to estimate the nitrogen contents of apple leaves based on hyperspectral models. In the three models, the 4-3-1 BP neural network model of shoot growth phenophase was the best estimation model. The R2 of estimated value and measured value was 0.6307, RE% was 23.37, RMSE was 0.6274.

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